A Novel Strategy to Mitigate the Hyperinflammatory Response to COVID-19 by Targeting Leukotrienes
Bibliographic record
Abstract
SARS-CoV-2 causing Coronavirus disease 2019 (COVID-19) has wreaked havoc during the global pandemic of 2020 infecting millions and leaving over a half million dead. As a new virus, not previously in the human population, but with similarities to other coronaviruses causing severe acute respiratory distress syndrome (SARS/ARDS), and no known treatments, the race to re-purpose existing drugs and to enlist novel therapeutics is underway. In the half-year since the first cases, we have acquired substantial knowledge of this virus and the clinical course of COVID-19 progression. Results from early clinical trials have revealed two treatments (remdesivir, dexamethasone) that mitigate disease progression but clearly, there is much room for improvement. Initial case reports indicated many succumb to COVID-19 of hypoxic respiratory failure due to ARDS. However, ensuing studies revealed an atypical, immune cell-sequestered, vasculature-inflamed state leading to multi-organ thrombotic complications and end organ failure likely due to hyperinflammatory host responses. This Perspective focuses on a potential mechanism for a key COVID-19 disease progression turning point related to vascular and airway inflammation. The leukotriene lipid mediators have been overlooked with discussion centering on cytokine storms unleashing the deadly form of COVID-19. Leukotrienes possess some of the most potent known activities on immune cell trafficking and vascular leakage. We offer a simple treatment paradigm using two generic drugs targeting the hyperinflammatory response that characterizes the turning point from mild to severe/critical COVID-19 by targeting leukotriene biosynthesis with zileuton (Zyflo® controlled release formulation) and antagonism of the cysteinyl leukotriene 1 receptor with montelukast (Singulair®).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".